1. Field of the Invention
The present invention relates in general to processing digital data, and in particular, to a system and method for matching curves of multiple images representing a scene for stereo vision applications.
2. Related Art
For stereo vision applications, accurate vector or mathematical representations of objects or scenes derived from two dimensional image data are very important. Vector or mathematical representations of a scene can be comprised of curve information derived from an image. Reliable curve matching is a difficult problem but is required in many vision-based applications. Curve matching is particularly difficult when the edges in question, such as for a general scene, are not limited to be on straight lines.
Although many techniques exist for detecting the edges of a scene, these techniques, however, have difficulties when applied to more general scenes. For example, some techniques use line matching methods applied to scenes containing mainly planar surfaces. However, they are not suitable for scenes containing curves because the line model is insufficient to describe curves. In addition, they are not suitable for scenes that are taken by a camera close to the scene, where the local affinity or similarity assumption for long line segments is not valid. Further, the projection of straight lines in 3D onto images may no longer be straight due to radial lens distortion.
Some problems related to curve matching are the design of good unary and binary measurements, and the definition of appropriate similarity and compatibility functions. Previous techniques and methods for curve matching provided examples on the unary measurements and similarity function between curves. Nevertheless, these previous methods were limited when dealing with binary measurements and compatibility functions. Namely, their compatibility functions were usually computed from measurements such as disparity or disparity gradient, which are only suitable for the description of relationships between two pairs of points, and are not scale invariant.
Therefore, what is needed is a system and method that solves the problems that prior methods encounter with general scenes with a system that performs curve matching (including lines) within a probabilistic relaxation framework. What is also needed is a system the uses the relaxation framework to progressively reduce the matching.
To overcome the limitations in the prior art as described above and other limitations that will become apparent upon reading and understanding the present specification, the present invention is embodied in a system and method for matching curves of multiple images representing a scene. The curve matching produces a geometrical representation of the scene from the images, which can be used for any suitable application, such as computer and stereo vision applications.
In general, first, multiple images depicting a scene are digitally received by the system. The images are graphical images digitally received and processed. For example, the images can be two dimensional image data, such as bitmap or raster image data. Curves of the images are then matched to correlate the two images of the scene for creating three dimensional (3D) curve information, such as 3D vector or mathematical information, of the scene. This 3D vector information can then be used in any suitable manner, for example, to digitally reconstruct the scene for stereo vision applications.
The present invention performs the curve matching (including lines) preferably within a probabilistic relaxation framework. The relaxation framework is used to progressively reduce the matching ambiguity. In other words, the relaxation framework is used to handle the curves and explicitly model uncertainty in line segment measurements. Epipolar geometry can be used to reduce the matching ambiguity between the line segments. Similarity-invariant unary and binary measurements suitable for curves are developed. An additional measurement can be introduced to model the uncertainty of the binary measurements, which can then be used to compute the matching support from neighboring matches. The present invention also includes preprocessing techniques to enhance curve matching.